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基于AF-BiTCN的弹道中段目标HRRP识别

王晓丹 王鹏 宋亚飞 向前 李京泰

吴忠, 丑武胜. 考虑框架伺服特性时SGCMG系统操纵律设计[J]. 北京航空航天大学学报, 2004, 30(06): 489-492.
引用本文: 王晓丹,王鹏,宋亚飞,等. 基于AF-BiTCN的弹道中段目标HRRP识别[J]. 北京航空航天大学学报,2025,51(2):349-359 doi: 10.13700/j.bh.1001-5965.2023.0025
Wu Zhong, Chou Wusheng. Steering law design for SGCMGs taking gimbal servo characteristics into account[J]. Journal of Beijing University of Aeronautics and Astronautics, 2004, 30(06): 489-492. (in Chinese)
Citation: WANG X D,WANG P,SONG Y F,et al. HRRP recognition of midcourse ballistic targets based on AF-BiTCN[J]. Journal of Beijing University of Aeronautics and Astronautics,2025,51(2):349-359 (in Chinese) doi: 10.13700/j.bh.1001-5965.2023.0025

基于AF-BiTCN的弹道中段目标HRRP识别

doi: 10.13700/j.bh.1001-5965.2023.0025
基金项目: 国家自然科学基金(61876189,61703426,61273275);陕西省高校科协青年人才托举计划(20190108);陕西省创新人才推进计划(2020KJXX-065)
详细信息
    通讯作者:

    E-mail:afeu_wang@163.com

  • 中图分类号: TP183;TP391.4

HRRP recognition of midcourse ballistic targets based on AF-BiTCN

Funds: National Natural Science Foundation of China (61876189,61703426,61273275); Young Talent Fund of University Association for Science and Technology in Shaanxi, China (20190108); Innovation Talent Supporting Project of Shaanxi, China (2020KJXX-065)
More Information
  • 摘要:

    针对弹道中段目标高分辨距离像(HRRP)的时序特征提取和识别问题,为充分利用弹道中段目标HRRP的双向时序信息,进一步提高识别性能,提出一种基于加性融合双向时间卷积神经网络(AF-BiTCN)的识别方法。对HRRP数据采用双向时序滑窗法处理为双向序列;构建BiTCN逐层提取HRRP的双向深层时序特征,并将双向时序特征采用加性策略融合;利用更加稳健的融合特征实现对弹道中段目标的识别,并使用Adam算法优化AF-BiTCN的收敛速度和稳定性。实验结果表明:所提的基于AF-BiTCN的弹道中段目标HRRP识别方法较堆叠选择长短期记忆网络(SLSTM)、堆叠门控循环单元(SGRU)等6种时序方法具有更高的准确率和更快的识别速度,在测试集上达到了96.60%的准确率,并且在噪声数据集上表现出更好的鲁棒性。

     

  • 图 1  TCN架构

    Figure 1.  TCN architecture

    图 2  残差块示意图

    Figure 2.  Residual block

    图 3  AF-BiTCN结构

    Figure 3.  AF-BiTCN structure

    图 4  双向滑窗法示意图

    Figure 4.  Bidirectional sliding window method

    图 5  不同方法识别性能对比

    Figure 5.  Recognition performance comparison of different methods

    图 6  AF-BiTCN参数配置实验结果

    Figure 6.  Results of AF-BiTCN parameter configuration experiments

    图 7  消融实验结果

    Figure 7.  Ablation experiments results

    表  1  AF-BiTCN参数设置

    Table  1.   AF-BiTCN parameters setting

    参数 数值
    损失函数 交叉熵
    优化器 Adam[24]
    批量大小 256
    学习率 0.000 3
    一阶矩衰减率 0.9
    二阶矩衰减率 0.999
    滑窗窗长 32
    滑窗间隔 16
    迭代次数 400
    卷积核 256,128,64,64
    卷积核尺寸 1×3
    膨胀系数 1,2,8,16
    Dropout率 0.5
    下载: 导出CSV

    表  2  不同方法性能对比

    Table  2.   Performance comparison of different methods

    模型 准确率/% 参数量 识别速度/μs
    AF-BiTCN 96.60 1.16×106 334
    CNN-BiGRU 95.78 1.76×106 792
    Attention-BiGRU 95.57 1.15×106 708
    BiGRU 95.37 1.15×106 670
    BiLSTM 94.70 1.52×106 884
    SGRU 94.17 0.56×106 374
    SLSTM 93.87 0.58×106 392
    下载: 导出CSV

    表  3  不同信噪比条件下鲁棒性对比实验的识别准确率

    Table  3.   Recognition accuracy of robustness comparison experiments under different signal-to-noise ratios %

    信噪比/dB SLSTM SGRU BiLSTM BiGRU Attention-BiGRU CNN-BiGRU AF-BiTCN
    −10 56.69 56.08 54.00 56.11 56.22 55.58 56.88
    −5 66.02 66.00 67.67 68.25 67.81 66.56 69.22
    0 75.86 75.61 74.83 75.55 77.02 75.33 77.21
    5 82.83 81.61 81.00 82.22 83.13 82.94 83.64
    10 86.33 86.64 84.78 86.05 87.50 87.89 89.03
    20 89.91 91.86 88.33 90.97 91.61 92.36 92.45
    下载: 导出CSV

    表  4  不同融合方法的性能对比

    Table  4.   Performance comparison of different fusion methods %

    融合方法 弹道目标类别 准确率 精确率 召回率 F1分数
    加性融合 弹头 91.60 92.74 91.74 92.24
    高仿诱饵 95.92 93.04 95.91 94.46
    简单诱饵 97.33 98.40 97.25 97.82
    球形诱饵 100.00 100.00 100.00 100.00
    母舱 98.08 98.90 98.08 98.49
    同维度连接 弹头 89.75 93.24 89.82 91.50
    高仿诱饵 94.33 93.16 94.33 93.74
    简单诱饵 98.58 94.94 98.50 96.68
    球形诱饵 100.00 100.00 100.00 100.00
    母舱 97.92 99.23 97.91 98.57
    乘性融合 弹头 87.42 95.01 87.48 91.09
    高仿诱饵 95.75 92.28 95.75 93.98
    简单诱饵 99.33 93.27 99.25 96.16
    球形诱饵 100.00 100.00 100.00 100.00
    母舱 97.58 99.82 97.58 98.69
    下载: 导出CSV

    表  5  AF-BiTCN消融实验结果

    Table  5.   Ablation experiments results of AF-BiTCN %

    模型 准确率 召回率 精确率 F1分数
    AF-BiTCN 96.60 96.62 96.60 96.60
    正向TCN 96.00 96.00 96.01 95.98
    反向TCN 96.33 96.33 96.38 96.31
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-01-17
  • 录用日期:  2023-02-25
  • 网络出版日期:  2023-03-07
  • 整期出版日期:  2025-02-28

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